pyimgal 0.3.0

Python bindings for the imgal image processing and algorithm library.

pyimgal

Python bindings for the imgal image algorithm and processing library. Visit imgal.org for more information.

Installation

pyimgal from PyPI

You can install the pyimgal package from PyPI with:

$ pip install pyimgal

The pyimgal package is compatible with Python >=3.7 and the following architectures.

Operating System	Architecture
Linux	amd64, aarch64
macOS	intel, arm64
Windows	amd64

Alternatively, you can install pyimagal from source by building the imgal_python repository. See the next section for instructions.

Build pyimgal from source

To build the pyimgal Python package from source, use the maturin build tool (this requires the Rust toolchain to build the imgal core library). If you're using uv to manage your Python virtual environments (venv) add maturin to your environment and run the maturin develop --release command in the imgal_python directory of the imgal repository with your selected venv activated:

$ source ~/path/to/myenv/.venv/bin/activate
$ (myenv) cd imgal_python
$ maturin develop --release

Alternatively, if you're using conda or mamba you can do the following:

$ cd imgal_python
$ mamba activate myenv
(myenv) $ mamba install maturin
...
(myenv) $ maturin develop --release

This will install pyimgal in the currently active Python environment.

Usage

Using pyimgal

Once imgal_python has been installed in a compatible Python environment, imgal will be available to import. The example below demonstrates how to obtain a colocalization z-score (i.e. colocalization and anti-colocalization strength) using the Spatially Adaptive Colocalization Analysis (SACA) framework. The two number values after the channels are threshold values for channels a and b respectively.

Note: This example assumes you have 3D data (row, col, ch) to perform colocalization analysis and the tifffile package in your environment.

import imgal.colocalization as coloc
from tifffile import imread

# load some data
image = imread("path/to/data.tif")

# slice channels to perform colocalization analysis
ch_a = image[:, :, 0]
ch_b = image[:, :, 1]

# compute colocalization z-score with SACA 2D with 4 parallel threads
zscore = coloc.saca_2d(ch_a, ch_b, 525, 400, threads=4)

# apply Bonferroni correction and compute significant pixel mask
mask = coloc.saca_significance_mask(z_score)

Documentation

Each function in imgal is documented and published on docs.rs.

License

Imgal is a dual-licensed project with your choice of:

• MIT License (see LICENSE-MIT)
• The Unlicense (see LICENSE-UNLICENSE)